Photoacoustic (PA) imaging in the second near-infrared (NIR-II) window exhibits enhanced deep-tissue imaging capability. Likely, cancer therapy in the NIR-II window could provide deeper penetration depth and higher exposure to laser over NIR-I. However, the traditional application of excitation light is still in the NIR-I window. In view of the excellent imaging and therapeutic capabilities of NI…

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Photoacoustic (PA) imaging in the second near-infrared (NIR-II) window exhibits enhanced deep-tissue imaging capability. Likely, cancer therapy in the NIR-II window could provide deeper penetration depth and higher exposure to laser over NIR-I. However, the traditional application of excitation light is still in the NIR-I window. In view of the excellent imaging and therapeutic capabilities of NIR-II window, we have demonstrated a simple polyoxometalate (POM) clusters (molecular formula: (Na)n(PMo12O40) or (NH4+)n(PMo12O40)), which integrates NIR-II photoacoustic imaging and NIR-II photothermal therapy into an "all-in-one" theranostic nanoplatform, and could be used for PA imaging-guided photothermal therapy in the NIR-II window. In vivo experiments demonstrate that the POM clusters with good water solubility and biocompatibility were effective to kill tumor without recurrence and metastasis under 1064 nm laser illumination.

CONCLUSIONS: The proposed method segments the three fluids in the retina with high DSC value. Fine-tuning the networks trained on the RETOUCH dataset makes the network perform better and faster than training from scratch. Enriching the networks with inputting a variety of shapes by extracting patches helped to segment the fluids better than using a full image.

Volatile organic compounds (VOCs) exists ubiquitously in chemical industries and were regarded as major contributors to air pollution, which should be strictly regulated. Vacuum ultraviolet irradiation coupled with photocatalytic oxidation (VUV-PCO) has been considered as an efficient approach to VOCs removal due to high-energy photons which could break down VOCs directly and be absorbed by photo…

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Volatile organic compounds (VOCs) exists ubiquitously in chemical industries and were regarded as major contributors to air pollution, which should be strictly regulated. Vacuum ultraviolet irradiation coupled with photocatalytic oxidation (VUV-PCO) has been considered as an efficient approach to VOCs removal due to high-energy photons which could break down VOCs directly and be absorbed by photocatalysts to generate free radicals for further oxidation. However, the photochemical transformation mechanisms of VOCs have not been fully revealed. Herein, we systematically analyzed the intermediates using proton-transfer-reaction mass spectrometer (PTR-MS) to explore the transformation mechanisms of toluene degradation in VUV and VUV-PCO processes. VUV-PCO process displayed superior toluene degradation efficiency (50 %) and mineralization efficiency (65 %) compared with single VUV photolysis (35 %) and UV photocatalysis (5 %). TiO2 was deeply involved into CO2 generalization by amplifying the advantages of VUV system and further mineralizing the intermediates. In VUV and VUV-PCO processes, O2 participation changed the intermediates distribution by increasing multiple oxygenated products, while the introduction of water contributed to the formation and degradation of most intermediates. A possible degradation mechanism of toluene under VUV irradiation combined with TiO2 was proposed. This study provides a deep mechanistic insight into VOCs degradation by VUV-PCO process.

Photonics is among the most promising emerging technologies for providing fast and energy-efficient Deep Learning (DL) implementations. Despite their advantages, these photonic DL accelerators also come with certain important limitations. For example, the majority of existing photonic accelerators do not currently support many of the activation functions that are commonly used in DL, such as the …

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Photonics is among the most promising emerging technologies for providing fast and energy-efficient Deep Learning (DL) implementations. Despite their advantages, these photonic DL accelerators also come with certain important limitations. For example, the majority of existing photonic accelerators do not currently support many of the activation functions that are commonly used in DL, such as the ReLU activation function. Instead, sinusoidal and sigmoidal nonlinearities are usually employed, rendering the training process unstable and difficult to tune, mainly due to vanishing gradient phenomena. Thus, photonic DL models usually require carefully fine-tuning all their training hyper-parameters in order to ensure that the training process will proceed smoothly. Despite the recent advances in initialization schemes, as well as in optimization algorithms, training photonic DL models is still especially challenging. To overcome these limitations, we propose a novel adaptive initialization method that employs auxiliary tasks to estimate the optimal initialization variance for each layer of a network. The effectiveness of the proposed approach is demonstrated using two different datasets, as well as two recently proposed photonic activation functions and three different initialization methods. Apart from significantly increasing the stability of the training process, the proposed method can be directly used with any photonic activation function, without further requiring any other kind of fine-tuning, as also demonstrated through the conducted experiments.

Diffusion tensor magnetic resonance imaging (DTI) is unsurpassed in its ability to map tissue microstructure and structural connectivity in the living human brain. Nonetheless, the angular sampling requirement for DTI leads to long scan times and poses a critical barrier to performing high-quality DTI in routine clinical practice and large-scale research studies. In this work we present a new pro…

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Diffusion tensor magnetic resonance imaging (DTI) is unsurpassed in its ability to map tissue microstructure and structural connectivity in the living human brain. Nonetheless, the angular sampling requirement for DTI leads to long scan times and poses a critical barrier to performing high-quality DTI in routine clinical practice and large-scale research studies. In this work we present a new processing framework for DTI entitled DeepDTI that minimizes the data requirement of DTI to six diffusion-weighted images (DWIs) required by conventional voxel-wise fitting methods for deriving the six unique unknowns in a diffusion tensor using data-driven supervised deep learning. DeepDTI maps the input b=0 image and six DWI volumes sampled along optimized diffusion-encoding directions, along with T1-weighted and T2-weighted image volumes, to the residuals between the input and high-quality output image volumes using a 10-layer three-dimensional convolutional neural network (CNN). The inputs and outputs of DeepDTI are uniquely formulated, which not only enables residual learning to boost CNN performance but also enables tensor fitting of resultant high-quality DWIs to generate orientational DTI metrics for tractography. The very deep CNN used by DeepDTI leverages the redundancy in local and non-local spatial information and across diffusion-encoding directions and image contrasts in the data. The performance of DeepDTI was systematically quantified in terms of the quality of the output images, DTI metrics, DTI-based tractography and tract-specific analysis results. We demonstrate rotationally-invariant and robust estimation of DTI metrics from DeepDTI that are comparable to those obtained with two b=0 images and 21 DWIs for the primary eigenvector derived from DTI and two b=0 images and 26-30 DWIs for various scalar metrics derived from DTI, achieving 3.3-4.6× acceleration, and twice as good as those of a state-of-the-art denoising algorithm at the group level. The twenty major white-matter tracts can be accurately identified from the tractography of DeepDTI results. The mean distance between the core of the major white-matter tracts identified from DeepDTI results and those from the ground-truth results using 18 b=0 images and 90 DWIs measures around 1-1.5 mm. DeepDTI leverages domain knowledge of diffusion MRI physics and power of deep learning to render DTI, DTI-based tractography, major white-matter tracts identification and tract-specific analysis more feasible for a wider range of neuroscientific and clinical studies.

The advancement of artificial intelligence concurrent with the development of medical imaging techniques provided a unique opportunity to turn medical imaging from mostly qualitative, to further quantitative and mineable data that can be explored for the development of clinical decision support systems (cDSS). Radiomics, a method for the high throughput extraction of hand-crafted features from me…

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The advancement of artificial intelligence concurrent with the development of medical imaging techniques provided a unique opportunity to turn medical imaging from mostly qualitative, to further quantitative and mineable data that can be explored for the development of clinical decision support systems (cDSS). Radiomics, a method for the high throughput extraction of hand-crafted features from medical images, and deep learning -the data driven modeling techniques based on the principles of simplified brain neuron interactions, are the most researched quantitative imaging techniques. Many studies reported on the potential of such techniques in the context of cDSS. Such techniques could be highly appealing due to the reuse of existing data, automation of clinical workflows, minimal invasiveness, three-dimensional volumetric characterization, and the promise of high accuracy and reproducibility of results and cost-effectiveness. Nevertheless, there are several challenges that quantitative imaging techniques face, and need to be addressed before the translation to clinical use. These challenges include, but are not limited to, the explainability of the models, the reproducibility of the quantitative imaging features, and their sensitivity to variations in image acquisition and reconstruction parameters. In this narrative review, we report on the status of quantitative medical image analysis using radiomics and deep learning, the challenges the field is facing, propose a framework for robust radiomics analysis, and discuss future prospects.

Plant viral diseases represent a significant burden to plant health, and their highest impact in Mediterranean agriculture is on vegetables grown under intensive horticultural practices. In order to understand better virus evolution and emergence, the most prevalent viruses were mapped in the main cucurbitaceous (melon, squashes) and solanaceous (tomato, pepper) crops and in some wild hosts in th…

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Plant viral diseases represent a significant burden to plant health, and their highest impact in Mediterranean agriculture is on vegetables grown under intensive horticultural practices. In order to understand better virus evolution and emergence, the most prevalent viruses were mapped in the main cucurbitaceous (melon, squashes) and solanaceous (tomato, pepper) crops and in some wild hosts in the French Mediterranean area, and virus diversity, evolution and population structure were studied through molecular epidemiology approaches. Surveys were performed in summer 2016 and 2017, representing a total of 1530 crop samples and 280 weed samples. The plant samples were analysed using serological and molecular approaches, including high-throughput sequencing (HTS). The viral species and their frequency in crops were quite similar to those of surveys conducted ten years before in the same areas. Contrary to other Mediterranean countries, aphid-transmitted viruses remain the most prevalent in France whereas whitefly-transmitted ones have not yet emerged. However, NGS analysis of viral evolution revealed the appearance of undescribed viral variants, especially for watermelon mosaic virus (WMV) in cucurbits, or variants not present in France before, as for cucumber mosaic virus (CMV) in solanaceous crops. Deep sequencing also revealed complex virus populations within individual plants with frequent recombination or reassortment. The spatial genetic structure of cucurbit aphid-borne yellows virus (CABYV) was related to the landscape structure, whereas in the case of WMV, the recurrence of introduction events and probable human exchanges of plant material resulted in complex spatial pattern of genetic variation.

Stereotactic Radiosurgery has become the main treatment for patients with limited number of brain metastases (BM). Recently, with the increasing use of this modality, there is a growth in recurrence cases. Recurrence after radiation therapy can be divided in changes favoring either tumor recurrence or radiation necrosis (RN). Laser Interstitial Thermal Therapy (LITT) is minimally invasive treatme…

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Stereotactic Radiosurgery has become the main treatment for patients with limited number of brain metastases (BM). Recently, with the increasing use of this modality, there is a growth in recurrence cases. Recurrence after radiation therapy can be divided in changes favoring either tumor recurrence or radiation necrosis (RN). Laser Interstitial Thermal Therapy (LITT) is minimally invasive treatment modality that has been used to treat primary and metastatic brain tumors. When associated with real-time thermometry using Magnetic Resonance Imaging, the extent of ablation can be controlled to provide maximum coverage and avoid eloquent areas. The objective of this study was to investigate the use of LITT in the treatment of BM. An extensive review of the relevant literature was conducted and the outcome results are discussed. There is an emphasis on safety and local control rate of patients treated with this modality. The findings of our study suggest that LITT is a viable safe technique to treat recurrent BM, especially in patients with deep-seated lesions where surgical resection is not an option.

CONCLUSIONS: Modified sinustrabeculectomy with basal iridectomy in combination with deep sclerectomy and drainage of the anterior chamber and suprachoroidal space by autosclera helps achieve a persistent hypotensive effect and does not require the use of donor material.

CONCLUSIONS: The genetic and phenotypic diversity of pHGGs is now well characterized after large-scale sequencing studies on patient tissue. However, clinical treatment paradigms have not yet shifted in response to this information. Combination therapies targeting multiple kinases or epigenetic targets may hold more promise, especially if attempted in selected patient populations with hemispheric pHGG tumors and relevant targeted therapeutic biomarkers.

Lung cancer is the deadliest cancer worldwide. It has been shown that early detection using low-dose computer tomography (LDCT) scans can reduce deaths caused by this disease. We present a general framework for the detection of lung cancer in chest LDCT images. Our method consists of a nodule detector trained on the LIDC-IDRI dataset followed by a cancer predictor trained on the Kaggle DSB 2017 d…

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Lung cancer is the deadliest cancer worldwide. It has been shown that early detection using low-dose computer tomography (LDCT) scans can reduce deaths caused by this disease. We present a general framework for the detection of lung cancer in chest LDCT images. Our method consists of a nodule detector trained on the LIDC-IDRI dataset followed by a cancer predictor trained on the Kaggle DSB 2017 dataset and evaluated on the IEEE International Symposium on Biomedical Imaging (ISBI) 2018 Lung Nodule Malignancy Prediction test set. Our candidate extraction approach is effective to produce accurate candidates with a recall of 99.6%. In addition, our false positive reduction stage classifies successfully the candidates and increases precision by a factor of 2000. Our cancer predictor obtained a ROC AUC of 0.913 and was ranked 1st place at the ISBI 2018 Lung Nodule Malignancy Prediction challenge. Graphical abstract.

Femoral hernia is the protrusion of a peritoneal sac through the femoral ring into the femoral canal lying deep and inferior to the inguinal ligament. The hernia sac usually contains preperitoneal fat, omentum, bowel, or fluid. Ultrasound is recommended as the first-line investigation for diagnosing clinically occult femoral hernias in nonemergency settings, whereas CT is the imaging of choice in…

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Femoral hernia is the protrusion of a peritoneal sac through the femoral ring into the femoral canal lying deep and inferior to the inguinal ligament. The hernia sac usually contains preperitoneal fat, omentum, bowel, or fluid. Ultrasound is recommended as the first-line investigation for diagnosing clinically occult femoral hernias in nonemergency settings, whereas CT is the imaging of choice in emergency settings. High accuracy of the ultrasound in clinically occult femoral hernia is further validated with further CT and MRI. In this article, we propose sonographic detection of the physiological peritoneal fluid herniating through capacious femoral ring manifesting as a "speech bubble/speech box appearance." This is a potentially invaluable sonographic sign for clinically occult femoral hernias, differentiating them from inguinal hernias and cysts of the canal of Nuck in females and preventing inadvertent attempts to aspirate.

Hematopoietic stem cells (HSCs) are ultimately responsible for the lifelong renewal of all blood cell lineages. In the bone marrow (BM), HSCs reside in specialized microenvironments referred to as the "niche." HSC niche consists of complex components including heterogeneous cell populations, growth factors, and extracellular matrix molecules. The crosstalk between HSCs and their niche is essentia…

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Hematopoietic stem cells (HSCs) are ultimately responsible for the lifelong renewal of all blood cell lineages. In the bone marrow (BM), HSCs reside in specialized microenvironments referred to as the "niche." HSC niche consists of complex components including heterogeneous cell populations, growth factors, and extracellular matrix molecules. The crosstalk between HSCs and their niche is essential to regulate the survival, self-renewal, migration, quiescence, and differentiation of HSCs. The application of mice models with endogenous ablation of specific cell types, advanced imaging technologies, high-throughput single-cell RNA sequencing, and single-cell mass cytometry methods have provided deep insights into communications between HSCs and niche cells. In this chapter, we have focused on three important cell types in the BM niche: mesenchymal stem cells (MSCs), osteoblasts (OBs), and endothelial cells (ECs). In order to address the interaction between HSCs and these three cell populations in BM niche, we have described methodology for (1) collecting total BM from femur and tibia of C57BL/6 mice; (2) analyzing or sorting of MSCs, OBs, and ECs based on the selection of surface markers CD45, Ter119, CD31, Sca1, and CD51 with flow cytometry; and (3) co-culturing the sorted cells with purified HSCs for further functional assays of HSCs.

This paper presents a fabrication method for glassy carbon neural electrode arrays that combines 3D printing and chemical pyrolysis technology. The carbon electrodes have excellent biological compatibility and can be used in neural signal recording. A pretreated Si wafer is used as the substrate for 3D printing, and then, stereolithography 3D printing technology is employed to print photosensitiv…

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This paper presents a fabrication method for glassy carbon neural electrode arrays that combines 3D printing and chemical pyrolysis technology. The carbon electrodes have excellent biological compatibility and can be used in neural signal recording. A pretreated Si wafer is used as the substrate for 3D printing, and then, stereolithography 3D printing technology is employed to print photosensitive resin into a cone shape. Next, chemical pyrolysis is applied to convert the 3D prints into glassy carbon electrodes and modify the electrochemical performance of the carbon electrodes. Finally, the glassy carbon electrodes are packed with conductive wires and PDMS. The proposed fabrication method simplifies the manufacturing process of carbon materials, and electrodes can be fabricated without the need of deep reactive ion etching (DRIE). The height of the carbon electrodes is 1.5 mm, and the exposure area of the tips is 0.78 mm2, which is convenient for the implantation procedure. The specific capacitance of the glassy carbon arrays is higher than that of a platinum electrode (9.18 mF/cm2 vs 3.32 mF/cm2, respectively), and the impedance at 1 kHz is lower (7.1 kΩ vs 8.8 kΩ). The carbon electrodes were tested in vivo, and they showed excellent performance in neural signal recording. The signal-to-noise ratio of the carbon electrodes is 50.73 ± 6.11, which is higher than that of the Pt electrode (20.15 ± 5.32) under the same testing conditions. The proposed fabrication method of glassy carbon electrodes provides a novel approach to manufacture penetrating electrodes for nerve interfaces in biomedical engineering and microelectromechanical systems.

Health informatics and biomedical computing have introduced the use of computer methods to analyze clinical information and provide tools to assist clinicians during the diagnosis and treatment of diverse clinical conditions. With the amount of information that can be obtained in the healthcare setting, new methods to acquire, organize, and analyze the data are being developed each day, including…

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Health informatics and biomedical computing have introduced the use of computer methods to analyze clinical information and provide tools to assist clinicians during the diagnosis and treatment of diverse clinical conditions. With the amount of information that can be obtained in the healthcare setting, new methods to acquire, organize, and analyze the data are being developed each day, including new applications in the world of big data and machine learning. In this review, first we present the most basic concepts in data science, including the structural hierarchy of information and how it is managed. A section is dedicated to discussing topics relevant to the acquisition of data, importantly the availability and use of online resources such as survey software and cloud computing services. Along with digital datasets, these tools make it possible to create more diverse models and facilitate collaboration. After, we describe concepts and techniques in machine learning used to process and analyze health data, especially those most widely applied in rheumatology. Overall, the objective of this review is to aid in the comprehension of how data science is used in health, with a special emphasis on the relevance to the field of rheumatology. It provides clinicians with basic tools on how to approach and understand new trends in health informatics analysis currently being used in rheumatology practice. If clinicians understand the potential use and limitations of health informatics, this will facilitate interdisciplinary conversations and continued projects relating to data, big data, and machine learning.

CONCLUSIONS: The incidence of symptomatic VTE after surgery for proximal humerus fractures is low. Chemical VTE prophylaxis in patients after surgical fixation for proximal humerus fractures is not universally indicated. Selective prophylaxis for patients with systemic risk factors may be warranted.

CONCLUSIONS: The dMCL is the largest medial restraint to tibial external rotation in extension. Therefore, following a combined ACL + MCL injury, AMRI may persist if there is inadequate healing of both the sMCL and dMCL, and MCL deficiency increases the risk of ACL graft failure.

CONCLUSIONS: We recommend the determination of D‑dimer levels and, in the case of elevated levels, the broad indication for compression sonography of the deep leg veins on admission of patients with suspected or confirmed SARS-CoV2. In this way DVT in the setting of CoViD-19 can be recognized early and therapeutic anticoagulation can be started. All inpatient CoViD-19 patients should receive thrombosis prophylaxis with low molecular weight heparin. Further studies on point of care methods (TEG®, ROTEM®) for the detection of hypercoagulability in SARS-CoV2 are necessary.

Metaproteomics of gut microbiomes from animal hosts lacking a reference genome is challenging. Here we describe a strategy combining high-resolution metaproteomics and host RNA sequencing (RNA-seq) with generalist database searching to survey the digestive tract of Gammarus fossarum, a small crustacean used as a sentinel species in ecotoxicology. This approach provides a deep insight into the ful…

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Metaproteomics of gut microbiomes from animal hosts lacking a reference genome is challenging. Here we describe a strategy combining high-resolution metaproteomics and host RNA sequencing (RNA-seq) with generalist database searching to survey the digestive tract of Gammarus fossarum, a small crustacean used as a sentinel species in ecotoxicology. This approach provides a deep insight into the full range of biomasses and metabolic activities of the holobiont components, and differentiates between the intestine and hepatopancreatic caecum.

Acute pulmonary embolism (PE) is the third most common acute cardiovascular condition, and its prevalence increases over time. D-dimer has a very high negative predictive value, and if normal levels of D-dimer are detected, the diagnosis of PE is very unlikely. The final diagnosis should be confirmed by computed tomographic scan. However, echocardiography is the most available, bedside, low-cost,…

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Acute pulmonary embolism (PE) is the third most common acute cardiovascular condition, and its prevalence increases over time. D-dimer has a very high negative predictive value, and if normal levels of D-dimer are detected, the diagnosis of PE is very unlikely. The final diagnosis should be confirmed by computed tomographic scan. However, echocardiography is the most available, bedside, low-cost, diagnostic procedure for patients with PE. Risk stratification is of utmost importance and is mainly based on hemodynamic status of the patient. Patients with PE and hemodynamic stability require further risk assessment, based on clinical symptoms, imaging, and circulating biomarkers.

A thorough medical history is critical in patient selection for local anesthesia facelifting. Patients with no prior issues with dental procedures and no history of significant anxiety are better candidates. Simplifying local anesthesia mixtures and using dilute concentrations will minimize dosing errors and decrease risk of local anesthesia toxicity. Oral anxiolytics can be used with caution to …

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A thorough medical history is critical in patient selection for local anesthesia facelifting. Patients with no prior issues with dental procedures and no history of significant anxiety are better candidates. Simplifying local anesthesia mixtures and using dilute concentrations will minimize dosing errors and decrease risk of local anesthesia toxicity. Oral anxiolytics can be used with caution to minimize patient anxiety. Pulse oximetry, telemetry, and blood pressure monitoring should be performed with any addition of oral or IV sedation/anxiolytic. The short-scar anterior facelift is ideal for local anesthesia due to the limited deep-plane dissection and shorter procedure duration.

Traditional superficial musculoaponeurotic system (SMAS) facelifting surgery uses a laminar surgical dissection. This approach does not treat areas of facial volume loss, and requires additional volume supplementation with fat grafting or fillers. The novel volumizing extended deep-plane facelift uses a composite approach to the facelift flap. By incorporating a platysma myotomy in the extended d…

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Traditional superficial musculoaponeurotic system (SMAS) facelifting surgery uses a laminar surgical dissection. This approach does not treat areas of facial volume loss, and requires additional volume supplementation with fat grafting or fillers. The novel volumizing extended deep-plane facelift uses a composite approach to the facelift flap. By incorporating a platysma myotomy in the extended deep-plane flap, a novel composite transposition flap can be created that revolumizes the posterior jawline, recreating a defined convex jawline of youth. Special attention is paid to the deep anatomy of the face, and the need for release of the facial ligaments.

This article provides the facial plastic surgeon with anatomic and embryologic evidence supporting use of the deep-plane technique and understanding the evolution of the technique over decades to the vertical platysma advancement for optimal treatment of facial aging. The original description of the deep-plane rhytidectomy described a basic subsuperficial musculoaponeurotic system dissection in t…

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This article provides the facial plastic surgeon with anatomic and embryologic evidence supporting use of the deep-plane technique and understanding the evolution of the technique over decades to the vertical platysma advancement for optimal treatment of facial aging. The original description of the deep-plane rhytidectomy described a basic subsuperficial musculoaponeurotic system dissection in the midface. This plane of dissection provides access to deeper anatomic structures. A detailed description of the procedure is provided to allow safe and consistent performance. Insights into anatomic landmarks, technical nuances, and alternative approaches for facial variations are presented.

The various rhytidectomy techniques share a common goal of safe repositioning of the facial soft tissues with a lasting effect. This article reviews rhytidectomy approaches and the current methods and practice patterns of the senior author. It includes a discussion of the extended sub-superficial musculoaponeurotic system rhytidectomy technique, which, in the opinion of the senior author, offers …

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The various rhytidectomy techniques share a common goal of safe repositioning of the facial soft tissues with a lasting effect. This article reviews rhytidectomy approaches and the current methods and practice patterns of the senior author. It includes a discussion of the extended sub-superficial musculoaponeurotic system rhytidectomy technique, which, in the opinion of the senior author, offers the best result with respect to neck rhytids, cervicomental angle and jawline definition, and improvement of jowling. With its ability to be readily coupled with a deep plane dissection, when indicated, this technique becomes indispensable in the facial plastic surgeon's armamentarium.

Rhytidectomy techniques have evolved since the early 1900s. As the understanding of facial anatomy and the aging process expanded, the superficial musculoaponeurotic system (SMAS) became a focal point in developing longer-lasting, natural results. Further evolution led to various approaches in repositioning the SMAS layer, including subperiosteal, composite, and deep plane rhytidectomies. This ar…

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Rhytidectomy techniques have evolved since the early 1900s. As the understanding of facial anatomy and the aging process expanded, the superficial musculoaponeurotic system (SMAS) became a focal point in developing longer-lasting, natural results. Further evolution led to various approaches in repositioning the SMAS layer, including subperiosteal, composite, and deep plane rhytidectomies. This article describes the nuances of SMAS rhytidectomy, the biplanar SMAS imbrication technique, and adjuvant procedures used. This biplanar SMAS technique has been refined over more than 25 years and has proved to be a reliable and safe technique that leads to high patient satisfaction with minimal complications.